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Tensorflow¶

  • Keras Multi-layer Perceptron

    • Keras Multi-layer Perceptron (MLP) for Binary Classifications
    • Keras Multi-layer Perceptron (MLP) for Multi-Class Classifications
    • Keras Multi-layer Perceptron (MLP) for Image Classifications
    • Keras Multi-layer Perceptron (MLP) for Regression

  • Tensorflow Premade Estimators

    • Tensorflow Linear Classifier
    • Tensorflow DNN Classifier
    • Tensorflow Boosted Trees Classifier
    • Tensorflow Boosted Trees Classifier with Feature Importance Analysis

  • Tensorflow Image and Text Classification

    • Image Augmentations with TensorFlow
    • Image Classification with TensorFlow
    • Text Classification Pipeline with Tensorflow

Tensorflow Linear Classifier

In this article, we demonstrate implementing the Tensorflow Linear classifier model by an example. The details regarding this dataset can be found in the Diagnostic Wisconsin Breast Cancer Database.

Dataset¶

In [1]:
import numpy as np
import pandas as pd
from sklearn import datasets

data = datasets.load_breast_cancer()
Data = pd.DataFrame(data['data'], columns = [x.title() for x in data['feature_names']])
Labels_dict = dict(zip(list(np.sort(np.unique(data['target'].tolist()))),
                       list([x.title() for x in data['target_names']])))
Target = 'Diagnosis'
Data[Target] = data['target']
# Data['Diagnosis'] = data['target'].replace(Labels_dict)
display(Data)
print(data['DESCR'])
Mean Radius Mean Texture Mean Perimeter Mean Area Mean Smoothness Mean Compactness Mean Concavity Mean Concave Points Mean Symmetry Mean Fractal Dimension ... Worst Texture Worst Perimeter Worst Area Worst Smoothness Worst Compactness Worst Concavity Worst Concave Points Worst Symmetry Worst Fractal Dimension Diagnosis
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.30010 0.14710 0.2419 0.07871 ... 17.33 184.60 2019.0 0.16220 0.66560 0.7119 0.2654 0.4601 0.11890 0
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.08690 0.07017 0.1812 0.05667 ... 23.41 158.80 1956.0 0.12380 0.18660 0.2416 0.1860 0.2750 0.08902 0
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.19740 0.12790 0.2069 0.05999 ... 25.53 152.50 1709.0 0.14440 0.42450 0.4504 0.2430 0.3613 0.08758 0
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.24140 0.10520 0.2597 0.09744 ... 26.50 98.87 567.7 0.20980 0.86630 0.6869 0.2575 0.6638 0.17300 0
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.19800 0.10430 0.1809 0.05883 ... 16.67 152.20 1575.0 0.13740 0.20500 0.4000 0.1625 0.2364 0.07678 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
564 21.56 22.39 142.00 1479.0 0.11100 0.11590 0.24390 0.13890 0.1726 0.05623 ... 26.40 166.10 2027.0 0.14100 0.21130 0.4107 0.2216 0.2060 0.07115 0
565 20.13 28.25 131.20 1261.0 0.09780 0.10340 0.14400 0.09791 0.1752 0.05533 ... 38.25 155.00 1731.0 0.11660 0.19220 0.3215 0.1628 0.2572 0.06637 0
566 16.60 28.08 108.30 858.1 0.08455 0.10230 0.09251 0.05302 0.1590 0.05648 ... 34.12 126.70 1124.0 0.11390 0.30940 0.3403 0.1418 0.2218 0.07820 0
567 20.60 29.33 140.10 1265.0 0.11780 0.27700 0.35140 0.15200 0.2397 0.07016 ... 39.42 184.60 1821.0 0.16500 0.86810 0.9387 0.2650 0.4087 0.12400 0
568 7.76 24.54 47.92 181.0 0.05263 0.04362 0.00000 0.00000 0.1587 0.05884 ... 30.37 59.16 268.6 0.08996 0.06444 0.0000 0.0000 0.2871 0.07039 1

569 rows × 31 columns

.. _breast_cancer_dataset:

Breast cancer wisconsin (diagnostic) dataset
--------------------------------------------

**Data Set Characteristics:**

    :Number of Instances: 569

    :Number of Attributes: 30 numeric, predictive attributes and the class

    :Attribute Information:
        - radius (mean of distances from center to points on the perimeter)
        - texture (standard deviation of gray-scale values)
        - perimeter
        - area
        - smoothness (local variation in radius lengths)
        - compactness (perimeter^2 / area - 1.0)
        - concavity (severity of concave portions of the contour)
        - concave points (number of concave portions of the contour)
        - symmetry
        - fractal dimension ("coastline approximation" - 1)

        The mean, standard error, and "worst" or largest (mean of the three
        worst/largest values) of these features were computed for each image,
        resulting in 30 features.  For instance, field 0 is Mean Radius, field
        10 is Radius SE, field 20 is Worst Radius.

        - class:
                - WDBC-Malignant
                - WDBC-Benign

    :Summary Statistics:

    ===================================== ====== ======
                                           Min    Max
    ===================================== ====== ======
    radius (mean):                        6.981  28.11
    texture (mean):                       9.71   39.28
    perimeter (mean):                     43.79  188.5
    area (mean):                          143.5  2501.0
    smoothness (mean):                    0.053  0.163
    compactness (mean):                   0.019  0.345
    concavity (mean):                     0.0    0.427
    concave points (mean):                0.0    0.201
    symmetry (mean):                      0.106  0.304
    fractal dimension (mean):             0.05   0.097
    radius (standard error):              0.112  2.873
    texture (standard error):             0.36   4.885
    perimeter (standard error):           0.757  21.98
    area (standard error):                6.802  542.2
    smoothness (standard error):          0.002  0.031
    compactness (standard error):         0.002  0.135
    concavity (standard error):           0.0    0.396
    concave points (standard error):      0.0    0.053
    symmetry (standard error):            0.008  0.079
    fractal dimension (standard error):   0.001  0.03
    radius (worst):                       7.93   36.04
    texture (worst):                      12.02  49.54
    perimeter (worst):                    50.41  251.2
    area (worst):                         185.2  4254.0
    smoothness (worst):                   0.071  0.223
    compactness (worst):                  0.027  1.058
    concavity (worst):                    0.0    1.252
    concave points (worst):               0.0    0.291
    symmetry (worst):                     0.156  0.664
    fractal dimension (worst):            0.055  0.208
    ===================================== ====== ======

    :Missing Attribute Values: None

    :Class Distribution: 212 - Malignant, 357 - Benign

    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian

    :Donor: Nick Street

    :Date: November, 1995

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass.  They describe
characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree.  Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

.. topic:: References

   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction 
     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on 
     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
     San Jose, CA, 1993.
   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and 
     prognosis via linear programming. Operations Research, 43(4), pages 570-577, 
     July-August 1995.
   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 
     163-171.

As can be seen, the number of instances is 569 and the number of attributes is 32. The object of the exercise is to create a classification model that can classify the type of Diagnosis base on the rest of the attributes. However, first, let's plot a count plot for Diagnosis attribute.

Features with high variance¶

Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance.

In [2]:
from sklearn import preprocessing

X = Data.drop(columns = [Target])
y = Data[Target]
Temp = X.var().to_frame(name= 'Variance (Origial)').round(4)
scaler = preprocessing.StandardScaler()
X_std = scaler.fit_transform(X)
X_std = pd.DataFrame(data = X_std, columns = X.columns)
Temp = Temp.join(X_std.var().to_frame(name= 'Variance (Normalized)').round(4))
display(Temp.style.background_gradient(cmap='Reds', subset = 'Variance (Origial)')\
        .background_gradient(cmap='Greens', subset = 'Variance (Normalized)'))
del Temp
  Variance (Origial) Variance (Normalized)
Mean Radius 12.418900 1.001800
Mean Texture 18.498900 1.001800
Mean Perimeter 590.440500 1.001800
Mean Area 123843.554300 1.001800
Mean Smoothness 0.000200 1.001800
Mean Compactness 0.002800 1.001800
Mean Concavity 0.006400 1.001800
Mean Concave Points 0.001500 1.001800
Mean Symmetry 0.000800 1.001800
Mean Fractal Dimension 0.000000 1.001800
Radius Error 0.076900 1.001800
Texture Error 0.304300 1.001800
Perimeter Error 4.087900 1.001800
Area Error 2069.431600 1.001800
Smoothness Error 0.000000 1.001800
Compactness Error 0.000300 1.001800
Concavity Error 0.000900 1.001800
Concave Points Error 0.000000 1.001800
Symmetry Error 0.000100 1.001800
Fractal Dimension Error 0.000000 1.001800
Worst Radius 23.360200 1.001800
Worst Texture 37.776500 1.001800
Worst Perimeter 1129.130800 1.001800
Worst Area 324167.385100 1.001800
Worst Smoothness 0.000500 1.001800
Worst Compactness 0.024800 1.001800
Worst Concavity 0.043500 1.001800
Worst Concave Points 0.004300 1.001800
Worst Symmetry 0.003800 1.001800
Worst Fractal Dimension 0.000300 1.001800
In [3]:
X_std.columns = [x.replace(' ','_').replace('/','_') for x in X_std.columns]
Feat_Dict = dict(zip(X.columns, X_std.columns))

Train and Test sets¶

In [4]:
import plotly.express as px
from HD_DeepLearning import DatasetTargetDist

Pull = [0 for x in range((len(Labels_dict)-1))]
Pull.append(.05)
PD = dict(PieColors = ['SeaGreen','FireBrick'], TableColors = ['Navy','White'], hole = .4,
          column_widths=[0.6, 0.4], textfont = 14, height = 400, tablecolumnwidth = [0.25, 0.15, 0.15],
          pull = Pull, legend_title = Target, title_x = 0.5, title_y = .9, pie_legend = [0.01, 0.01])
del Pull
DatasetTargetDist(Data, Target, Labels_dict, PD, orientation= 'columns')

StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

In [5]:
from sklearn.model_selection import StratifiedShuffleSplit

def HD_StratifiedShuffleSplit(X, y, Test_Size = 0.3):
    sss = StratifiedShuffleSplit(n_splits=1, test_size=Test_Size, random_state=42)
    _ = sss.get_n_splits(X, y)
    for train_index, test_index in sss.split(X, y):
        # X
        if isinstance(X, pd.DataFrame):
            X_train, X_test = X.loc[train_index], X.loc[test_index]
        else:
            X_train, X_test = X[train_index], X[test_index]
        # y    
        if isinstance(y, pd.Series):
            y_train, y_test = y[train_index], y[test_index]
        else:
            y_train, y_test = y[train_index], y[test_index]
    del sss
    return X_train, y_train, X_test, y_test

X_train, y_train, X_test, y_test = HD_StratifiedShuffleSplit(X_std, y)

from HD_DeepLearning import Train_Test_Dist  
PD.update(dict(column_widths=[0.3, 0.3, 0.3], tablecolumnwidth = [0.2, 0.4], height = 550, legend_title = Target))

Train_Test_Dist(X_train, y_train, X_test, y_test, PD, Labels_dict)
#
import tensorflow as tf
# y_train = tf.keras.utils.to_categorical(y_train, num_classes=len(Labels_dict))
# y_test = tf.keras.utils.to_categorical(y_test, num_classes=len(Labels_dict))

Modeling: Tensorflow Linear Classifier¶

Here, we use the Tensorflow Linear classifier model.tf.estimator.LinearClassifier.

Input Function The input_function defines how data is converted to a tf.data.Dataset that provides the input pipeline.
  • features - A Python dictionary in which:
    • Each key is the name of a feature.
    • Each value is an array containing all of that feature's values.
  • label - An array containing the values of the label for every example.
In [6]:
def input_fn(features, labels, training=True, batch_size=256):
    """An input function for training or evaluating"""
    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

    # Shuffle and repeat if you are in training mode.
    if training:
        dataset = dataset.shuffle(1000).repeat()
    
    return dataset.batch(batch_size)

Moreover, an estimator model consists of two main parts, feature columns, and a numeric vector. Feature columns provide explanations for the input numeric vector. The following function separates categorical and numerical columns (features)and returns a descriptive list of feature columns.

In [7]:
import tensorflow as tf

def NumCat_fun(Inp):
    Temp = Inp.dtypes.reset_index(drop = False)
    Temp.columns = ['Features', 'Data Type']
    Temp['Data Type'] = Temp['Data Type'].astype(str)
    
    # Numeric_Columns
    Numeric_Columns = Temp.loc[Temp['Data Type'].isin(['int64', 'int32', 'float64', 'float32']),'Features'].tolist()
    # Categorical_Columns
    Categorical_Columns = Temp.loc[Temp['Data Type'] == 'object','Features'].tolist()
    return Numeric_Columns, Categorical_Columns

def Feat_Columns(Inp):
    Numeric_Columns, Categorical_Columns = NumCat_fun(Inp)
        
    # Feature Columns
    feature_columns = []
    if len(Categorical_Columns)>0:
        for feature_name in Categorical_Columns:
            vocabulary = Inp[feature_name].unique()
            feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
    if len(Numeric_Columns)>0:
        for feature_name in Numeric_Columns:
            feature_columns.append(tf.feature_column.numeric_column(feature_name))
    return feature_columns

my_feature_columns = Feat_Columns(X_std)

Estimator using the Default Optimizer¶

In [8]:
from IPython.display import clear_output
tf.keras.backend.clear_session()
IT = int(5e2)
classifier = tf.estimator.LinearClassifier(feature_columns=my_feature_columns,
                                        # The model must choose between 3 classes.
                                        n_classes=len(Labels_dict))
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
accuracy accuracy_baseline auc auc_precision_recall average_loss label/mean loss precision prediction/mean recall global_step
0.9708 0.6257 0.9977 0.9986 0.0632 0.6257 0.0632 0.9904 0.6122 0.9626 500

ROC Curves¶

In [9]:
from HD_DeepLearning import ROC_Curve

# converting y_test to categorical
y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes = len(Labels_dict), dtype='float32')

pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])    
ROC_Curve(y_test_cat, probs, n_classes = len(Labels_dict), FS = 8)

Confusion Matrix¶

The confusion matrix allows for visualization of the performance of an algorithm. Note that due to the size of data, here we don't provide a Cross-validation evaluation. In general, this type of evaluation is preferred.

In [10]:
from sklearn import metrics
from HD_DeepLearning import Confusion_Mat

# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
                                                           output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
                                                          output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)

Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
                                                 
display(Reports_Train.style.hide(axis='index').set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
        set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide(axis='index').set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
        set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))

PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
Train Set precision recall f1-score support
Malignant 0.993103 0.972973 0.982935 148.000000
Benign 0.984190 0.996000 0.990060 250.000000
accuracy 0.987437 0.987437 0.987437 0.987437
macro avg 0.988647 0.984486 0.986497 398.000000
weighted avg 0.987504 0.987437 0.987410 398.000000
Test Set precision recall f1-score support
Malignant 0.940299 0.984375 0.961832 64.000000
Benign 0.990385 0.962617 0.976303 107.000000
accuracy 0.970760 0.970760 0.970760 0.970760
macro avg 0.965342 0.973496 0.969068 171.000000
weighted avg 0.971639 0.970760 0.970887 171.000000
Estimator using the FTRL Optimizer with Regularization

The Follow the Regularized Leader (FTRL) model is an implementation of the FTRL-Proximal online learning algorithm for binomial logistic regression (for details see [6]).

In [11]:
tf.keras.backend.clear_session()
IT = int(5e3)
classifier = tf.estimator.LinearClassifier(feature_columns=my_feature_columns,
                          optimizer=tf.keras.optimizers.Ftrl(learning_rate=0.1, l1_regularization_strength=0.001))
#
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
accuracy accuracy_baseline auc auc_precision_recall average_loss label/mean loss precision prediction/mean recall global_step
0.9825 0.6257 0.9978 0.9986 0.0553 0.6257 0.0553 0.9906 0.6166 0.9813 5000

ROC Curves¶

In [12]:
# converting y_test to categorical
y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes = len(Labels_dict), dtype='float32')

pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])    
ROC_Curve(y_test_cat, probs, n_classes = len(Labels_dict), FS = 8)

Confusion Matrix¶

In [13]:
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
                                                           output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
                                                          output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)

Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
                                                 
display(Reports_Train.style.hide(axis='index').set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
        set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide(axis='index').set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
        set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))

PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
Train Set precision recall f1-score support
Malignant 0.993151 0.979730 0.986395 148.000000
Benign 0.988095 0.996000 0.992032 250.000000
accuracy 0.989950 0.989950 0.989950 0.989950
macro avg 0.990623 0.987865 0.989213 398.000000
weighted avg 0.989975 0.989950 0.989936 398.000000
Test Set precision recall f1-score support
Malignant 0.969231 0.984375 0.976744 64.000000
Benign 0.990566 0.981308 0.985915 107.000000
accuracy 0.982456 0.982456 0.982456 0.982456
macro avg 0.979898 0.982842 0.981330 171.000000
weighted avg 0.982581 0.982456 0.982483 171.000000
Estimator using an Optimizer with a Learning Rate Decay
In [14]:
tf.keras.backend.clear_session()
IT = int(5e3)
classifier = tf.estimator.LinearClassifier(feature_columns=my_feature_columns,
            optimizer=lambda: tf.keras.optimizers.Adam(learning_rate=tf.compat.v1.train.exponential_decay(learning_rate=0.1,
            global_step=tf.compat.v1.train.get_global_step(), decay_steps=IT,decay_rate=0.96)))
#
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
accuracy accuracy_baseline auc auc_precision_recall average_loss label/mean loss precision prediction/mean recall global_step
0.9532 0.6257 0.9554 0.9621 0.46 0.6257 0.46 0.9626 0.617 0.9626 5000

ROC Curves¶

In [15]:
# converting y_test to categorical
y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes = len(Labels_dict), dtype='float32')

pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])    
ROC_Curve(y_test_cat, probs, n_classes = len(Labels_dict), FS = 8)

Confusion Matrix¶

In [16]:
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
                                                           output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
                                                          output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)

Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
                                                 
display(Reports_Train.style.hide(axis='index').set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
        set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide(axis='index').set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
        set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))

PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
Train Set precision recall f1-score support
Malignant 1.000000 1.000000 1.000000 148.000000
Benign 1.000000 1.000000 1.000000 250.000000
accuracy 1.000000 1.000000 1.000000 1.000000
macro avg 1.000000 1.000000 1.000000 398.000000
weighted avg 1.000000 1.000000 1.000000 398.000000
Test Set precision recall f1-score support
Malignant 0.937500 0.937500 0.937500 64.000000
Benign 0.962617 0.962617 0.962617 107.000000
accuracy 0.953216 0.953216 0.953216 0.953216
macro avg 0.950058 0.950058 0.950058 171.000000
weighted avg 0.953216 0.953216 0.953216 171.000000

References¶

  1. Regression analysis Wikipedia page
  2. Tensorflow tutorials
  3. W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993.
  4. O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and prognosis via linear programming. Operations Research, 43(4), pages 570-577, July-August 1995.
  5. W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 163-171.
  6. Online machine learning Wikipedia page
  7. Learning rate Wikipedia page